In this paper, an online Neural Inverse Controller is used to deal with actuator faults. In such a way that the Inverse of the nonlinear induction furnace system (IFS) is used as a fault-tolerant Controller (FTC) so that it can cover the fault of the actuator. The design is such that an online Neural network is used to model the NIFC, the three-layer Neural network is converted into a four-layer RBF Neural network, and the last layer is the nonlinear IFS, and this layer is It is unchangeable and the Controller and the system are connected and finally form a four-layer Neural network. So, an intelligent Inverse model of the IFS is used as FTC to cover the actuator fault of the nonlinear IFC. This Controller design is done in two ways: in the first part, five inputs are used for training the Neural network, one of which is the Neural network training error, but in the second part, in addition to the five inputs of the first part, the derivative of the error is used. And the error integral has also been used in Neural network training and the advantage of the second plan is to reduce overshoot. Finally, a fault actuator is applied to the nonlinear IFS in the 10th to the 30th second, despite the presence of the intelligent FTC, this defect is covered in less than one second, and the system continues to function normally despite the operator's defect in this interval of time.